Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole – up?

BMC Infectious Diseases

Research Article

Magnus Evander¹  Hussein Khalil²  Magnus Magnusson²  Birger Hörnfeldt²  Gert Olsson²  Frauke Ecke³ 

[1] Department of Clinical Microbiology, Virology, Umeå University, 901 85, Umeå, Sweden;Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Skogmarksgränd, 901 83, Umeå, Sweden;Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Skogmarksgränd, 901 83, Umeå, Sweden;Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Gerda Nilssons väg 5, 756 51, Uppsala, Sweden;

关键词: Bank vole;    Boosted regression trees;    Hantavirus;    Machine learning;    Micro-habitat;    Prediction, Puumala virus;    Validation;    Zoonotic hazard;   

DOI  :  10.1186/s12879-017-2618-z

 received in 2016-12-21, accepted in 2017-07-18,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundTo predict the risk of infectious diseases originating in wildlife, it is important to identify habitats that allow the co-occurrence of pathogens and their hosts. Puumala hantavirus (PUUV) is a directly-transmitted RNA virus that causes hemorrhagic fever in humans, and is carried and transmitted by the bank vole (Myodes glareolus). In northern Sweden, bank voles undergo 3–4 year population cycles, during which their spatial distribution varies greatly.MethodsWe used boosted regression trees; a technique inspired by machine learning, on a 10 – year time-series (fall 2003–2013) to develop a spatial predictive model assessing seasonal PUUV hazard using micro-habitat variables in a landscape heavily modified by forestry. We validated the models in an independent study area approx. 200 km away by predicting seasonal presence of infected bank voles in a five-year-period (2007–2010 and 2015).ResultsThe distribution of PUUV-infected voles varied seasonally and inter-annually. In spring, micro-habitat variables related to cover and food availability in forests predicted both bank vole and infected bank vole presence. In fall, the presence of PUUV-infected voles was generally restricted to spruce forests where cover was abundant, despite the broad landscape distribution of bank voles in general. We hypothesize that the discrepancy in distribution between infected and uninfected hosts in fall, was related to higher survival of PUUV and/or PUUV-infected voles in the environment, especially where cover is plentiful.ConclusionsMoist and mesic old spruce forests, with abundant cover such as large holes and bilberry shrubs, also providing food, were most likely to harbor infected bank voles. The models developed using long-term and spatially extensive data can be extrapolated to other areas in northern Fennoscandia. To predict the hazard of directly transmitted zoonoses in areas with unknown risk status, models based on micro-habitat variables and developed through machine learning techniques in well-studied systems, could be used.

【 授权许可】

CC BY   
© The Author(s). 2017